Machine learning builds a model based on training data. This does not require explicit knowledge represention of rules or facts, instead the algorithm creates its own representation. Some machine learning algorithms produce human-undertandable representations, such as decison trees, others, including neural neyworks are harder to interpret and may need explainable AI technques.
Machine learning typically progresses in three phases: training phase, validation phase and application phase.
Used in Chap. 1: page 7; Chap. 2: page 24; Chap. 3: page 27; Chap. 6: pages 90, 92, 93; Chap. 7: pages 100, 102, 104, 106, 107; Chap. 8: pages 113, 116, 117, 118; Chap. 10: pages 143, 144, 145, 147, 150, 151, 152, 155; Chap. 11: pages 159, 160; Chap. 12: pages 197, 198; Chap. 13: page 217; Chap. 14: pages 223, 233, 234; Chap. 16: pages 255, 258, 266, 267; Chap. 17: page 269; Chap. 18: pages 291, 297, 300, 302, 303, 310; Chap. 19: pages 313, 314, 315, 318, 319, 321, 322, 323, 324, 326; Chap. 20: pages 333, 334, 335, 336, 337, 341, 343, 344, 345, 346; Chap. 21: pages 352, 353, 355; Chap. 22: pages 368, 374, 375, 376; Chap. 23: pages 388, 391, 392; Chap. 24: page 400
Phases of machine learning